Method of decaying chrominance in images

ABSTRACT

A method and system for decaying chrominance. One or more processors obtain a selected one of a series of root images of a digital video. The selected root image includes root pixels each associated with color values. The processor(s) selects one of the root pixels until each of the root pixels has been selected. The color values associated with the selected root pixel are expressible as a color vector with a plurality of elements each storing a different one of the color values. The processor(s) determines a perceptual luminance value for the selected root pixel, generates a monochromic vector for the selected root pixel, generates a biased monochromic vector by multiplying the monochromic vector with a bias, and generates new color values associated with a new pixel of a denoised image corresponding to the selected root pixel by blending the biased monochromic vector with the color vector.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/468,063, filed on Mar. 7, 2017, and U.S. Provisional Application No.62/468,874, filed on Mar. 8, 2017, both of which are incorporated hereinby reference in their entireties.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention is directed generally to methods of reducing orremoving chromatic noise in images and digital video.

Description of the Related Art

Luminance noise refers to fluctuations in brightness. Luminance noisemay appear as light and dark specks (e.g., within a region of an imagein which pixels should have the same or similar brightness). Chromaticor chroma noise refers to fluctuations in color. Chroma noise may appearas specks or blotches of unexpected color(s) (e.g., within a region ofan image in which pixels should have the same or similar colors). Chromanoise is often more apparent in very dark or very light areas of animage and may give the image an unnatural appearance.

Image editing software often includes a user input (e.g., slider) thatmay be used to remove chroma noise manually. Software may alsoautomatically remove chroma noise by decolorizing any pixels that havean unexpected color when compared to their neighboring pixels.Decolorized pixels are set to black, which essentially converts thechroma noise to luminance noise. Then, other image processing techniquesmay be applied to the image to remove the luminance noise and improvethe overall appearance of the image.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a functional block diagram of a video capture system.

FIG. 2 is a flow diagram of a method of generating a denoised imageperformable by the video capture system.

FIG. 3 is a functional block diagram illustrating an exemplary mobilecommunication device that may be used to implement the video capturesystem.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a video capture system 200 configured to capturedigital video 203, which may be referred to as an image stream. Forexample, the digital video 203 may be captured and/or processed as aReal-Time Messaging Protocol (“RTMP”) video stream. By way of anon-limiting example, the video capture system 200 may be implemented asa mobile communication device 140 (described below and illustrated inFIG. 3). The video capture system 200 includes a housing 202, a camera204, one or more processors 206, memory 208, a display 210, and one ormore manual controls 220. The camera 204, the processor(s) 206, thememory 208, and the display 210 may be connected together by a bus 212(e.g., like a bus system 186 illustrated in FIG. 3).

The camera 204 is mounted on the housing 202. The camera 204 isconfigured to capture the digital video 203 and store that digital video203 in the memory 208. The captured digital video 203 includes a seriesof root images (e.g., including a root image 240) of a scene. By way ofa non-limiting example, the camera 204 may be implemented as a camera orvideo capture device 158 (see FIG. 3).

The processor(s) 206 is/are configured to execute software instructionsstored in the memory 208. By way of a non-limiting example, theprocessor(s) 206 may be implemented as a central processing unit (“CPU”)150 (see FIG. 3) and the memory 208 may be implemented as memory 152(see FIG. 3).

The display 210 is positioned to be viewed by the user while the useroperates the video capture system 200. The display 210 is configured todisplay a preview of the digital video 203 being captured by the camera204. By way of a non-limiting example, the display 210 may beimplemented as conventional display device, such as a touch screen. Thedisplay 210 may be mounted on the housing 202. For example, the display210 may be implemented as a display 154 (see FIG. 3). Alternatively, thedisplay 210 may be implemented as an electronic viewfinder, an auxiliarymonitor connected to the video capture system 200, and the like.

The manual control(s) 220 is/are configured to be operated by the userand may affect properties (e.g., focus, exposure, and the like) of thedigital video 203 being captured. The manual control(s) 220 may beimplemented as software controls that generate virtual controlsdisplayed by the display 210. In such embodiments, the display 210 maybe implemented as touch screen configured to receive user input thatmanually manipulates the manual control(s) 220. Alternatively, themanual control(s) 220 may be implemented as physical controls (e.g.,button, knobs, and the like) disposed on the housing 202 and configuredto be manually manipulated by the user. In such embodiments, the manualcontrol(s) 220 may be connected to the processor(s) 206 and the memory208 by the bus 212.

By way of non-limiting examples, the manual control(s) 220 may include afocus control 220A, an exposure control 220B, and the like. The focuscontrol 220A may be used to change the focus of the digital video beingcaptured by the camera 204. The exposure control 220B may change an ISOvalue, shutter speed, aperture, or an exposure value (“EV”) of thedigital video being captured by the camera 204.

The memory 208 stores a noise decay module 230 implemented by theprocessor(s) 206. In some embodiments, the noise decay module 230 maygenerate and display the virtual controls implementing the manualcontrol(s) 220. Alternatively, the manual control(s) 220 may beimplemented by other software instructions stored in the memory 208.

FIG. 2 is a flow diagram of a method 280 performed by the noise decaymodule 230 (see FIG. 1). Referring to FIG. 1, the method 280 (see FIG.2) generates the denoised image 250 from one of the series of rootimages of the digital video 203. For ease of illustration, the method280 (see FIG. 2) will be described as generating the denoised image 250from the root image 240.

In first block 282 (see FIG. 2), the noise decay module 230 obtains theroot image 240 as a raw bitmap (e.g., directly the camera 204 ) beforethe root image 240 is encoded. The root image 240 includes a pluralityof root pixels each associated with one or more color values within acolor space (e.g., a standard Red Green Blue (“SRGB”) color space). Inthis example, the RGB color values of each root pixel include separatevalues for red (“R_(srgb)”), green (“G_(srgb)”), and blue (“B_(srgb)”).However, through application of ordinary skill in the art to the presentteachings, the method 280 may be adapted for use with other colorspaces, such as HSL (Hue, Saturation, Lightness), HSV (Hue, Saturation,Value), and the like.

In decision block 284 (see FIG. 2), the noise decay module 230determines whether the root image 240 has linearized gamma values. Inother words, has the root image 240 not yet been gamma corrected? Thedecision in decision block 284 (see FIG. 2) is “YES,” when the rootimage 240 has linearized gamma values, meaning the root image 240 notyet been gamma corrected. Otherwise, the decision in decision block 284(see FIG. 2) is “NO.”

When the decision in decision block 284 (see FIG. 2) is “YES,” the noisedecay module 230 advances to block 288 (see FIG. 2). On the other hand,when the decision in decision block 284 (see FIG. 2) is “NO,” in block286 (see FIG. 2), the noise decay module 230 remaps the root image 240to linear gamma (e.g., using a shader or a lookup table). For example,if the root pixels are in the SRGB color space and the RGB values(R_(srgb), G_(srgb), and B_(srgb)) are scaled to range from 0 to 1, thefollowing formulas may be used to obtain the linear RGB values(R_(linear), G_(linear), and B_(linear)) for each root pixel in the rootimage 240:

$\begin{matrix}{R_{linear} = \left\{ \begin{matrix}{\frac{R_{srgb}}{12.92},{R_{srgb} \leq 0.04045}} \\{\left( \frac{R_{srgb} + 0.055}{1.055} \right)^{2.4},{R_{srgb} > 0.04045}}\end{matrix} \right.} & {{{Eq}.\; 1}R} \\{G_{linear} = \left\{ \begin{matrix}{\frac{G_{srgb}}{12.92},{G_{srgb} \leq 0.04045}} \\{\left( \frac{G_{srgb} + 0.055}{1.055} \right)^{2.4},{G_{srgb} > 0.04045}}\end{matrix} \right.} & {{{Eq}.\; 1}G} \\{B_{linear} = \left\{ \begin{matrix}{\frac{B_{srgb}}{12.92},{B_{srgb} \leq 0.04045}} \\{\left( \frac{B_{srgb} + 0.055}{1.055} \right)^{2.4},{B_{srgb} > 0.04045}}\end{matrix} \right.} & {{{Eq}.\; 1}B}\end{matrix}$

Then, the noise decay module 230 advances to block 288 (see FIG. 2).

At this point, the noise decay module 230 processes each root pixel ofthe root image 240 one at a time. Thus, in block 288 (see FIG. 2), thenoise decay module 230 selects one of the root pixels.

Then, in block 290 (see FIG. 2), the noise decay module 230 calculates aperceptual luminance (“p”) for the selected root pixel. The perceptualluminance (“p”) may be calculated by first calculating a relativeluminance (“Y”) for the selected root pixel. The relative luminance(“Y”) refers to the brightness of the selected root pixel.

The relative luminance (“Y”) of a particular pixel may be calculatedusing the following function in which a variable “s” represents thethree linearized RGB color values (R_(linear), G_(linear), andB_(linear)) of the particular pixel expressed as an RGB vector:

$\quad\begin{matrix}\begin{matrix}{Y = {{dot}\left( {s,{{vec}\; 3\left( {0.2126,0.7152,0.0722} \right)}} \right)}} \\{Y = {\left\lbrack {R_{linear},G_{linear},B_{linear}} \right\rbrack \cdot \left\lbrack {0.2126,07152,0.0722} \right\rbrack}} \\{= {\left( {R_{linear} \times 0.2126} \right) + \left( {G_{linear} \times 0.7152} \right) + \left( {B_{linear} \times 0.0722} \right)}}\end{matrix} & {{Eq}.\; 2}\end{matrix}$

Using the above equation, the relative luminance (“Y”) may be calculatedfor each pixel in a two-dimensional region of the root image 240centered at the selected root pixel. For example, the region may bethree pixels by three pixels. In this example, the selected root pixelmay be characterized as being an origin of the region (which includesthe root pixel and its eight surrounding neighbors) and assigned acoordinate value of (0, 0). Thus, a separate relative luminance valuemay be calculated for each of the eight root pixels neighboring theselected root pixel as well as for the selected root pixel. In thisexample, the following set of nine relative luminance values would becalculated: Y_((−1 ,−1)), Y_((−1,0)), Y_((−1,1)), Y_((0,−1)), Y_((0,0)),Y_((0,1)), Y_((1,−1)), Y_((1,0)), and Y_((1,1)). Then, these relativeluminance values may be combined to determine the relative luminance(“Y”) of the selected root pixel. For example, an average or a median ofthe relative luminance values may be calculated and used as the relativeluminance (“Y”) of the selected root pixel.

If the color values of the selected root pixel (represented by the RGBvector “s”) are linear, the perceptual luminance (“p”) of the selectedroot pixel equals the relative luminance (“Y”) of the selected rootpixel. Otherwise, the relative luminance (“Y”) may be linearized toobtain the perceptual luminance (“p”) using the following formula:

$\begin{matrix}{p = \left( \frac{\left( {Y + 0.055} \right)}{1.055} \right)^{2.4}} & {{Eq}.\; 3}\end{matrix}$

The perceptual luminance (“p”) in the RGB color space may be used by themethod 280 (see FIG. 2) for two reasons. First, the human eye is vastlymore sensitive to green than any other color and the RGB perceptualluminance easily accounts for this sensitivity. Second, digital imagesensors (e.g., included in the camera 204 ) that include an RGB colorfilter array (“CFA”) configuration produce green channels that are lowerin noise than their red and blue counterparts. By using the perceptualluminance (“p”) to determine chrominance decay (or desaturate the rootimage 240), the method 280 (see FIG. 2) spares (or causes less decay in)higher-quality green-dominant colors in the root image 240.

Next, in block 292 (see FIG. 2), the noise decay module 230 creates alinear monochromatic RGB vector by setting the value of each of the R,G,and B elements of the linear monochromatic RGB vector equal to theperceptual luminance (“p”).

linear monochromatic RGB vector=[p, p, p]  Eq. 4

In block 294 (see FIG. 2), the noise decay module 230 multiplies thelinear monochromatic RGB vector by a relative-luminance weightedsaturation bias (“o”) to obtain a biased monochromatic RGB vector.

biased monochromatic RGB vector=[o*p, o*p, o*p]  Eq. 5

The relative-luminance weighted saturation bias (“o”) may be calculatedusing the following formula:

o=0.16667×In(p)+1.0  Eq. 6

In block 296 (see FIG. 2), the noise decay module 230 generates a newpixel of the denoised image 250 with new (desaturated) color values byblending the biased monochromatic RGB vector ([o*p, o*p, o*p]) with theRGB vector ([R_(linear), G_(linear), B_(linear)]) of the selected rootpixel. In other words, the biased monochromatic RGB vector is multipliedby a first weight and the RGB vector is multiplied by a second weightwherein the first and second weights total one. The new color values areless saturated than the original color values associated with theselected root pixel. In particular, dim or less bright areas are moredesaturated than brighter areas. Thus, the method 280 may becharacterized as desaturating the selected root pixel and/or applying aweighted saturation to the selected root pixel.

Next, in decision block 298 (see FIG. 2), the noise decay module 230determines whether all of the root pixels of the root image 240 havebeen selected in block 288 (see FIG. 2). The decision in decision block298 (see FIG. 2) is “YES,” when the noise decay module 230 has not yetselected all of the root pixels. When the decision in decision block 298(see FIG. 2) is “YES,” the noise decay module 230 returns to block 288and selects a next root pixel from the root image 240.

On the other hand, the decision in decision block 298 (see FIG. 2) is“NO,” when the noise decay module 230 has selected all of the rootpixels. When the decision in decision block 298 (see FIG. 2) is “NO,”the method 280 (see FIG. 2) terminates.

At this point, a new pixel has been generated for each of the rootpixels. Combined, the new pixels define the denoised image 250.Optionally, the denoised image 250 may be remapped to a different colorspace. For example, the linear RGB values may be remapped to the sRGBcolor space. The denoised image 250 may be subject to one or moreadditional operations, such as Gamma curve remapping, luma curveaugmentation (shadow/highlight repair), histogram equalization,additional spacial denoise, RGB mixing, and lookup table application.Optionally, the denoised image 250 may be displayed to the user usingthe display 210.

The method 280 (see FIG. 2) desaturates the root image 240 (or linearbitmap) using the perceptual luminance (“p”) assigned to each root pixelto reduce or minimize chroma noise in critically underexposed (or dark)areas of the root image 240. Darker regions are desaturated more thanlighter areas, which may be characterized as progressively desaturatingthe very darkest pixels (where chrominance typically decomposes in lowbit-depth images).

Referring to FIG. 2, the method 280 does not evaluate high-frequencychrominance of either the root pixel selected in block 288 or itsneighborhood. Instead, the method 280 assumes that the occurrence ofchrominant anomalies (or chroma noise) progressively increases as theperceptual luminance (“p”) of the selected root-pixel (or itsneighborhood) approaches zero. Therefore, the method 280 evaluates onlythe perceptual luminance (“p”) of the selected root pixel (which may bethe median relative luminance of its spatial neighborhood). The visualreduction of chrominance noise in darker sectors of the root image 240is an incidental byproduct of the progressive desaturation process.

The method 280 decays the chrominance of the root image 240 andgenerates the denoised image 250 within the gamut of the original colorspace (e.g., the sRGB color space) of the root image 240.

Mobile Communication Device

FIG. 3 is a functional block diagram illustrating a mobile communicationdevice 140. The mobile communication device 140 may be implemented as acellular telephone, smart phone, a tablet computing device, aself-contained camera module (e.g., a wired web camera or an ActionCamera module), and the like. By way of a non-limiting example, themobile communication device 140 may be implemented as a smartphoneexecuting IOS or Android OS. The mobile communication device 140 may beconfigured to capture the digital video 203 (see FIG. 1) and process thedigital video 203 as a RTMP protocol video stream.

The mobile communication device 140 includes the CPU 150. Those skilledin the art will appreciate that the CPU 150 may be implemented as aconventional microprocessor, application specific integrated circuit(ASIC), digital signal processor (DSP), programmable gate array (PGA),or the like. The mobile communication device 140 is not limited by thespecific form of the CPU 150.

The mobile communication device 140 also contains the memory 152. Thememory 152 may store instructions and data to control operation of theCPU 150. The memory 152 may include random access memory, ready-onlymemory, programmable memory, flash memory, and the like. The mobilecommunication device 140 is not limited by any specific form of hardwareused to implement the memory 152. The memory 152 may also be integrallyformed in whole or in part with the CPU 150.

The mobile communication device 140 also includes conventionalcomponents, such as a display 154 (e.g., operable to display thedenoised image 250), the camera or video capture device 158, and keypador keyboard 156. These are conventional components that operate in aknown manner and need not be described in greater detail. Otherconventional components found in wireless communication devices, such asUSB interface, Bluetooth interface, infrared device, and the like, mayalso be included in the mobile communication device 140. For the sake ofclarity, these conventional elements are not illustrated in thefunctional block diagram of FIG. 3.

The mobile communication device 140 also includes a network transmitter162 such as may be used by the mobile communication device 140 fornormal network wireless communication with a base station (not shown).FIG. 3 also illustrates a network receiver 164 that operates inconjunction with the network transmitter 162 to communicate with thebase station (not shown). In a typical embodiment, the networktransmitter 162 and network receiver 164 are implemented as a networktransceiver 166. The network transceiver 166 is connected to an antenna168. Operation of the network transceiver 166 and the antenna 168 forcommunication with a wireless network (not shown) is well-known in theart and need not be described in greater detail herein.

The mobile communication device 140 may also include a conventionalgeolocation module (not shown) operable to determine the currentlocation of the mobile communication device 140.

The various components illustrated in FIG. 3 are coupled together by thebus system 186. The bus system 186 may include an address bus, data bus,power bus, control bus, and the like. For the sake of convenience, thevarious busses in FIG. 3 are illustrated as the bus system 186.

The memory 152 may store instructions executable by the CPU 150. Theinstructions may implement portions of one or more of the methodsdescribed above (e.g., the method 280 illustrated in FIG. 2). Suchinstructions may be stored on one or more non-transitory computer orprocessor readable media.

The foregoing described embodiments depict different componentscontained within, or connected with, different other components. It isto be understood that such depicted architectures are merely exemplary,and that in fact many other architectures can be implemented whichachieve the same functionality. In a conceptual sense, any arrangementof components to achieve the same functionality is effectively“associated” such that the desired functionality is achieved. Hence, anytwo components herein combined to achieve a particular functionality canbe seen as “associated with” each other such that the desiredfunctionality is achieved, irrespective of architectures or intermedialcomponents. Likewise, any two components so associated can also beviewed as being “operably connected,” or “operably coupled,” to eachother to achieve the desired functionality.

While particular embodiments of the present invention have been shownand described, it will be obvious to those skilled in the art that,based upon the teachings herein, changes and modifications may be madewithout departing from this invention and its broader aspects and,therefore, the appended claims are to encompass within their scope allsuch changes and modifications as are within the true spirit and scopeof this invention. Furthermore, it is to be understood that theinvention is solely defined by the appended claims. It will beunderstood by those within the art that, in general, terms used herein,and especially in the appended claims (e.g., bodies of the appendedclaims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations).

Accordingly, the invention is not limited except as by the appendedclaims.

The invention claimed is:
 1. A system comprising: memory storing adigital video and a noise decay module, the digital video comprising aseries of root images, the noise decay module comprising instructions;and at least one processor configured to execute the instructions thatwhen executed cause the at least one processor to obtain a selected oneof the series of root images and generate a denoised image based on theselected root image, the selected root image comprising a plurality ofroot pixels each associated with a set of color values, for each rootpixel in the plurality of root pixels, the at least one processorgenerating the denoised image by: determining a perceptual luminancevalue for the root pixel based on (a) the set of color values associatedwith the root pixel and (b) the set of color values associated with eachof a predetermined number of root pixels neighboring the root pixel, theset of color values associated with the root pixel being expressible asa color vector with a first plurality of elements, each of the firstplurality of elements storing a different one of the set of colorvalues, generating a monochromic vector for the root pixel, themonochromic vector having a second plurality of elements, each of thesecond plurality of elements equaling the perceptual luminance value,generating a biased monochromic vector by multiplying monochromic vectorwith a bias calculated as a function of the perceptual luminance value,and generating a new set of color values associated with a new pixel ofthe denoised image by blending the biased monochromic vector with thecolor vector.
 2. The system of claim 1, further comprising: a cameraconfigured to capture the digital video and store the digital video inthe memory.
 3. The system of claim 2, wherein the instructions cause theat least one processor to obtain each of the series of root images oneat a time and generate a different denoised image for each of the seriesof root images in real-time as the digital video is captured.
 4. Thesystem of claim 1, wherein when each of the plurality of root pixels ofthe selected root image does not have linearized gamma values, theinstructions cause the at least one processor to remap each value in theset of color values of each of the plurality of root pixels of theselected root image to a corresponding linear color value beforegenerating the denoised image.
 5. The system of claim 4, wherein theselected root image is in a Standard Red Blue Green (“sRGB”) color spacebefore the remapping occurs.
 6. The system of claim 1, wherein theinstructions, when executed by the at least one processor, cause the atleast one processor to remap the denoised image to a Standard Red BlueGreen (sRGB) color space after the denoised image is generated.
 7. Thesystem of claim 1, wherein the predetermined number of root pixels andthe root pixel comprise nine root pixels.
 8. The system of claim 1,wherein the root pixel and the predetermined number of root pixels areregion pixels, and the perceptual luminance value for the root pixel isdetermined by: determining a plurality of relative luminance values bycalculating a relative luminance value for each of the region pixels,and determining a median of the plurality of relative luminance values,the perceptual luminance value for the root pixel being the median. 9.The system of claim 8, wherein the relative luminance value isdetermined for each of the region pixels by: multiplying 0.2126 by a redcomponent of the set of color values associated with the region pixel,multiplying 0.7152 by a green component of the set of color valuesassociated with the region pixel, and multiplying 0.0722 by a bluecomponent of the set of color values associated with the region pixel.10. The system of claim 1, wherein the bias is calculated by multiplying0.16667 with a natural log of the perceptual luminance value to obtain aresult and adding one to the result.
 11. The system of claim 1implemented as a smartphone comprises: a camera configured to capturethe digital video and store the digital video in the memory, theinstructions causing the at least one processor to obtain each of theseries of root images one at a time and generate a different denoisedimage for each of the series of root images in real-time as the digitalvideo is captured.
 12. A method comprising: obtaining a selected one ofa series of root images of a digital video with at least one processor,the selected root image comprising a plurality of root pixels eachassociated with a set of color values; and until each of the pluralityof root pixels has been selected: selecting, with the at least oneprocessor, one of the root pixels, the set of color values associatedwith the selected root pixel being expressible as a color vector with afirst plurality of elements, each of the first plurality of elementsstoring a different one of the set of color values, determining, withthe at least one processor, a perceptual luminance value for theselected root pixel based on (a) the set of color values associated withthe selected rot pixel, and (b) the set of color values associated witheach of a predetermined number of root pixels neighboring the selectedroot pixel, generating, with the at least one processor, a monochromicvector for the selected root pixel, the monochromic vector having asecond plurality of elements, each of the second plurality of elementsequaling the perceptual luminance value, generating, with the at leastone processor, a biased monochromic vector by multiplying themonochromic vector with a bias calculated as a function of theperceptual luminance value, and generating, with the at least oneprocessor, a new set of color values associated with a new pixel of adenoised image corresponding to the selected root pixel by blending thebiased monochromic vector with the color vector.
 13. The method of claim12, further comprising: capturing the digital video with a camera; andstoring the digital video in a storage location accessible by the atleast one processor, the at least one processor obtaining the selectedroot image from the storage location.
 14. The method of claim 12,further comprising: determining, with the at least one processor,whether each of the plurality of root pixels of the selected root imagehas linearized gamma values; and when it is determined that theplurality of root pixels of the root image do not have linearized gammavalues, remapping each value in the set of color values of each of theplurality of root pixels of the selected root image to a correspondinglinear color value.
 15. The method of claim 14, wherein the selectedroot image is in a Standard Red Blue Green (sRGB) color space before theremapping occurs.
 16. The method of claim 12, further comprising:remapping, with the at least one processor, the denoised image to aStandard Red Blue Green (sRGB) color space after each of the pluralityof root pixels has been selected.
 17. The method of claim 12, whereinthe predetermined number of root pixels and the selected root pixelcomprise nine root pixels.
 18. The method of claim 12, wherein theselected root pixel and the predetermined number of root pixels areregion pixels, and the perceptual luminance value for the selected rootpixel is determined by: determining, with the at least one processor, aplurality of relative luminance values by calculating a relativeluminance value for each of the region pixels, and determining, with theat least one processor, a median of the plurality of relative luminancevalues, the perceptual luminance value for the selected root pixel beingthe median.
 19. The method of claim 18, wherein the relative luminancevalue is determined for each of the region pixels by: multiplying 0.2126by a red component of the set of color values associated with the regionpixel, multiplying 0.7152 by a green component of the set of colorvalues associated with the region pixel, and multiplying 0.0722 by ablue component of the set of color values associated with the regionpixel.
 20. The method of claim 12, wherein the bias is calculated bymultiplying 0.16667 with a natural log of the perceptual luminance valueto obtain a result and adding one to the result.